Data Quality Testing

Coalesce provides built-in testing capabilities to assess the quality of your data.

Data quality tests are crucial for identifying issues with data, such as missing values, incorrect data types, outliers, and violations of business rules. Testing data quality within Coalesce offers the advantage of validating transformed data against required standards before loading it into the target system. Integration of data testing into the existing data pipeline workflow enables early issue detection and ensures consistency across different data sources and transformations.

Some Things To Test For

  • Missing or null values: Test for columns that should not have null or missing values based on your business requirements.
  • Data types: Ensure that columns have the correct data types (e.g., strings, integers, dates) to avoid downstream issues.
  • Uniqueness: Test for columns or combinations of columns that should have unique values.
  • Value ranges: Validate that numerical or date columns fall within expected ranges based on your business rules.
  • Referential integrity: Test for foreign key relationships between tables to ensure data consistency.
  • Custom business rules: Implement tests that enforce specific business rules or data quality requirements specific to your organization.